GEN AI Developer

TP4 Developer

Key Responsibilities

• Generative AI Systems Design: Architect scalable, high-performance applications powered by Generative AI models (e.g., LLMs, diffusion models), with a focus on real-world usability and business integration.
• Prompt Engineering & RAG Pipelines: Design and implement optimized prompt strategies and Retrieval-Augmented Generation (RAG) workflows using LangChain and similar frameworks.
• Agentic AI & Autonomous Workflows: Develop and manage intelligent agents capable of multi-step reasoning, planning, and decision-making using frameworks like LangChain Agents or custom implementations.
• Vector Store Integration: Design and integrate vector databases for semantic search and document retrieval in GenAI workflows.
• End-to-End AI Application Development: Build and deploy AI-driven applications from scratch using Python and modern ML/AI toolkits. Ensure clean integration with APIs, databases, and frontend services.
• Microservices & API Development: Create modular, maintainable backend services using microservices architecture. Ensure RESTful APIs for smooth communication.
• CI/CD & DevOps: Develop and maintain CI/CD pipelines for model and app deployment. Ensure reproducibility, automated testing, and smooth rollout using tools like GitHub Actions, GitLab CI/CD, or Jenkins.
• Database Management: Use PostgreSQL and NoSQL databases effectively in storing structured and unstructured data, with focus on performance, indexing, and scaling.
• Performance Optimization: Continuously monitor, benchmark, and optimize applications for performance, scalability, and cost-efficiency across cloud and on-prem environments.
• Collaboration & Mentorship: Work closely with product managers, AI researchers, and junior developers. Lead by example and provide technical mentorship in AI system design and production-readiness.
  

Required Qualifications

• Technical Expertise:
• Strong proficiency in Python (mandatory) and experience with AI/ML libraries like FastAI, Hugging Face Transformers, and OpenAI APIs.
• Experience implementing LangChain workflows, prompt chaining, agents, tools, and memory systems.
• Hands-on experience building RAG systems using vector DBs like FAISS, Pinecone, Weaviate, or Chroma.
• Solid understanding of Generative AI technologies (LLMs, image/video generation, embeddings).
• Working knowledge of Agentic AI concepts for building autonomous tools and applications.
• Backend & Architecture:
• Strong skills in system design, API development, and microservices-based backend architecture.
• Experience with PostgreSQL and NoSQL databases (e.g., MongoDB, DynamoDB).
• Cloud & DevOps:
• Familiarity with CI/CD pipelines, containerization (Docker), and cloud deployments (AWS/GCP/Azure).
• Git-based version control and collaborative development workflows.
• Communication & Leadership:
• Ability to translate AI research into production-ready applications.
• Strong collaboration skills and experience leading engineering efforts or mentoring peers.
  

Preferred (Nice To Have)

• Experience working with LLMOps tools
• Exposure to data labeling, fine-tuning, or custom LLM training.
• Familiarity with streaming architectures or real-time AI inference.